{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "collapsed": true,
        "pycharm": {}
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "sns.set(rc\u003d{\"figure.figsize\": (6, 6)})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {}
      },
      "source": [
        "### 调色板 ###\n",
        "* 颜色很重要\n",
        "* color_palette()能传入任何Matplotlib所支持的颜色\n",
        "* color_palette()不写参数则默认颜色\n",
        "* set_palette()设置所有图的颜色\n",
        "\n",
        "### 分类色板 ###"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "collapsed": false,
        "pycharm": {}
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAABhCAYAAAAHpNImAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+3pLYMAAAAAASUVORK5CYII\u003d\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x2219bd8b4a8\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "current_palette \u003d sns.color_palette()\n",
        "sns.palplot(current_palette)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {}
      },
      "source": [
        "6个默认的颜色循环主题： deep, muted, pastel, bright, dark, colorblind"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {}
      },
      "source": [
        "### 圆形画板 ###\n",
        "\n",
        "当你有六个以上的分类要区分时，最简单的方法就是在一个圆形的颜色空间中画出均匀间隔的颜色(这样的色调会保持亮度和饱和度不变)。这是大多数的当他们需要使用比当前默认颜色循环中设置的颜色更多时的默认方案。\n",
        "\n",
        "最常用的方法是使用hls的颜色空间，这是RGB值的一个简单转换。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "collapsed": false,
        "pycharm": {}
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAABhCAYAAABGShAtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\u003d\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x221a0feda20\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.palplot(sns.color_palette(\"hls\", 8))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "collapsed": false,
        "pycharm": {}
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "\u003cmatplotlib.axes._subplots.AxesSubplot at 0x221a043f400\u003e"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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y3bHjTOcXZKPBf9ZZZ2nu3LlavXq1Vq1ape7ubj388MO666670m4jkUgqkUiaLAtAARoe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\nVwAAAABJRU5ErkJggg\u003d\u003d\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x221a0435518\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "data \u003d np.random.normal(size\u003d(20, 8)) + np.arange(8) / 2\n",
        "sns.boxplot(data\u003ddata,palette\u003dsns.color_palette(\"hls\", 8))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {}
      },
      "source": [
        "hls_palette()函数来控制颜色的亮度和饱和\n",
        "* l-亮度 lightness \n",
        "* s-饱和 saturation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "collapsed": false,
        "pycharm": {}
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAABhCAYAAABGShAtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAIQRgAAAYQQgAEAYAQgAEOYXrclwsTq5M6IAAAAASUVORK5CYII\u003d\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x221a0510748\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.palplot(sns.hls_palette(8, l\u003d.7, s\u003d.9))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "collapsed": false,
        "pycharm": {}
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAABhCAYAAABGShAtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYQQgAEAYAQgAEEYAAgCEEYAAAGG+A1QNagtJItv0AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x221a05c7588\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.palplot(sns.color_palette(\"Paired\",8))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {}
      },
      "source": [
        "###  使用xkcd颜色来命名颜色 ###\n",
        "xkcd包含了一套众包努力的针对随机RGB色的命名。产生了954个可以随时通过xdcd_rgb字典中调用的命名颜色。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "collapsed": false,
        "pycharm": {}
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[\u003cmatplotlib.lines.Line2D at 0x221a06bb828\u003e]"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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iqUrtleK5P3Me5+aqrP9Iz/7l56o693HI55J85j7lE/FXq6io0Nq1a1VUVKS8vDzV1NR0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\nAADCIhYAAEBYxAIAAAiLWAAAAGH9f40iyD3Dkp/HAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x221a06046d8\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot([0, 1], [0, 1], sns.xkcd_rgb[\"pale red\"], lw\u003d3)\n",
        "plt.plot([0, 1], [0, 2], sns.xkcd_rgb[\"medium green\"], lw\u003d3)\n",
        "plt.plot([0, 1], [0, 3], sns.xkcd_rgb[\"denim blue\"], lw\u003d3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "collapsed": false,
        "pycharm": {}
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAABhCAYAAAAa2uy9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg\u003d\u003d\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x221a05b1588\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "colors \u003d [\"windows blue\", \"amber\", \"greyish\", \"faded green\", \"dusty purple\"]\n",
        "sns.palplot(sns.xkcd_palette(colors))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {}
      },
      "source": [
        "###  连续色板 ###\n",
        "色彩随数据变换，比如数据越来越重要则颜色越来越深"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "collapsed": false,
        "pycharm": {}
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAABhCAYAAAAHpNImAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n92XLlQAAAABJRU5ErkJggg\u003d\u003d\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x221a0874f98\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.palplot(sns.color_palette(\"Blues\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {}
      },
      "source": [
        "如果想要翻转渐变，可以在面板名称中添加一个_r后缀"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "collapsed": false,
        "pycharm": {}
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAABhCAYAAAAHpNImAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMgAE+A0o21lWLPwc+wAAAABJRU5ErkJggg\u003d\u003d\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x221a0718400\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.palplot(sns.color_palette(\"BuGn_r\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {}
      },
      "source": [
        "###  cubehelix_palette()调色板 ###  \n",
        "色调线性变换"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "collapsed": false,
        "pycharm": {}
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAABhCAYAAABGShAtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x221a0749f28\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.palplot(sns.color_palette(\"cubehelix\", 8))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "collapsed": false,
        "pycharm": {}
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAABhCAYAAABGShAtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\u003d\u003d\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x221a07183c8\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.palplot(sns.cubehelix_palette(8, start\u003d.5, rot\u003d-.75))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "collapsed": false,
        "pycharm": {}
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAABhCAYAAABGShAtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg\u003d\u003d\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x221a03d00f0\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.palplot(sns.cubehelix_palette(8, start\u003d.75, rot\u003d-.150))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {}
      },
      "source": [
        "###  light_palette() 和dark_palette()调用定制连续调色板  ###"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "collapsed": false,
        "pycharm": {}
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAABhCAYAAAAHpNImAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg\u003d\u003d\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x221a0617f60\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": "sns.palplot(sns.light_palette(\"green\"))"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": "sns.palplot(sns.dark_palette(\"purple\"))",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%%\n"
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": "sns.palplot(sns.light_palette(\"navy\", reverse\u003dTrue))",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%%\n"
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": "x, y \u003d np.random.multivariate_normal([0, 0], [[1, -.5], [-.5, 1]], size\u003d300).T\npal \u003d sns.dark_palette(\"green\", as_cmap\u003dTrue)\nsns.kdeplot(x, y, cmap\u003dpal);\n",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%%\n"
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "collapsed": false,
        "pycharm": {}
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAABhCAYAAAAHpNImAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII\u003d\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x221a05b3c88\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": "sns.palplot(sns.light_palette((210, 90, 60), input\u003d\"husl\"))"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "pycharm": {}
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "anaconda-cloud": {},
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.5.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 1
}